Parameter Search Research Articles

Parametric Search for Optimal Channel Shape for Swirling Blood Flow in the Heart and Main Vessels.

The study presents a numerical parametric investigation of flow structures in channels with a longitudinal-radial profile zRN = Const and a spherical dome at the base. The goal of the study was to examine the flow structures in these channels depending on the exponent N of the profile and the height of the dome, to determine the conditions that provide optimal centripetal swirling flow, analogous to blood flow in the heart chambers and major vessels. The investigation was conducted using a comparative analysis of flow structures in channel configurations zRN = Const, carried out in two stages. In the first stage, the convergence parameter N was varied from 1.25 to 2.75 to identify the value that ensures optimal flow conditions. In the second stage, for the established value of N, the dome height was varied from 2.5 to 15 mm to identify the beneficial effects associated with its presence. The method of investigation involved numerical modeling in a steady-state regime. The results of the study on the influence of the convergence parameter revealed that the profile zR2 = Const provides optimal conditions for the formation of swirling flow with minimal specific losses and a uniform distribution of velocity gradients. This channel configuration also showed the best agreement with the analytical solutions for Burgers' vortex, confirming its effectiveness in the static approximation of flows. The parametric investigation of dome height indicated that an optimal dome height of 7 mm contributes to the smoothing of velocity gradients and the reduction of viscous losses due to the optimal enhancement of the centripetal swirling flow scale.

Research on music-based interventions for aphasia: a scoping review

ABSTRACT Background Over the past decade, the field of music neuroscience has surged in its examination of the efficacy of music rehabilitation. A growing area of interest within this surge has been research on music interventions for post-stroke aphasia. Brain activation during music processing and perception may overlap with activity during speech and language processing. This overlap suggests possible improvement of speech and language through activation of the multimodal music system. However, the role of music has not been investigated from the perspective of existing literature on skilled language treatment to identify gaps in evidence and outcomes. Aims We surveyed the current literature regarding the use of music-based methods for skilled aphasia rehabilitation to answer the question, “What research has been conducted on music-based, speech-language interventions for post-stroke aphasia?” Methods & procedures We conducted a systematic database search of PubMed, EBSCO Discovery Search, CINAHL Complete, CINAHL Plus with Full Text, MEDLINE Complete, MEDLINE, APA PsycArticles, and APA PsycInfo using the following search terms: (music) AND (aphasia) AND (treatment OR rehabilitation OR intervention OR therapy OR management OR approach) AND (music OR choir OR choral OR melodic OR melody OR sing OR singing OR song) AND (aphasia OR language OR dysphagia OR cognitive-communication). Outcome & results Twenty-seven studies met our inclusion criteria after full-text review. The primary use of music in skilled rehabilitative aphasia therapy and focus of music neurorehabilitation research for aphasia is melodic intonation therapy (MIT), and most studies that fell within the search parameters discussed the use, mechanisms, and adaptation of MIT within the aphasia rehabilitation space. Our review highlighted considerable inconsistency in both the administration of MIT and the reporting of characteristics of MIT used in each study. Sample sizes of included studies ranged from one to ten participants, and most studies were published from 2010 to the present. Conclusions The rise in music neurorehabilitation literature occurred without foundational research of music as an aphasia intervention to provide uniformity or example. Lack of standardization, variability of results, and inconsistency of reporting lessened the quality of evidence found in the included studies. There is a need in this field for standardization of practice and reporting, even in the exploration of new clinical applications of music interventions.

An ℓ1-plug-and-play approach for MPI using a zero shot denoiser with evaluation on the 3D open MPI dataset

Objective.Magnetic particle imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years. Among the benefits of MPI are its high temporal resolution, and that the technique does not expose the specimen to any kind of ionizing radiation. It is based on the non-linear response of magnetic nanoparticles to an applied magnetic field. From the electric signal measured in receive coils, the particle concentration has to be reconstructed. Due to the ill-posedness of the reconstruction problem, various regularization methods have been proposed for reconstruction ranging from early stopping methods, via classical Tikhonov regularization and iterative methods to modern machine learning approaches. In this work, we contribute to the latter class: we propose a Plug-and-Play approach based on a generic zero-shot denoiser with anℓ1-prior.Approach.We validate the reconstruction parameters of the method on a hybrid dataset and compare it with the baseline Tikhonov, ART, DIP and the previous PP-MPI, which is a Plug-and-Play method with denoiser trained on MPI-friendly data.Main results.We derive a Plug-and-Play reconstruction method based on a generic zero-shot denoiser. Addressing (hyper)parameter selection, we perform an extended parameter search on a hybrid validation dataset we produced and apply the derived parameters for reconstruction on the 3D Open MPI Dataset. We offer a quantitative and qualitative evaluation of the zero-shot Plug-and-Play approach on the 3D Open MPI dataset with the validated parameters. Moreover, we show the quality of the approach with different levels of preprocessing of the data.Significance.The proposed method employs a zero-shot denoiser which has not been trained for the MPI reconstruction task and therefore saves the cost for training. Moreover, it offers a method that can be potentially applied in future MPI contexts.

How knee muscles and ground reaction forces shape knee buckling and ankle push-off in neuromuscular simulations of human walking

Ankle push-off is important for efficient, human-like walking, and many prosthetic devices mimic push-off using motors or elastic elements. The knee is extended throughout the stance phase and begins to buckle just before push-off, with timing being crucial. However, the exact mechanisms behind this buckling are still unclear. We use a predictive neuromuscular simulation to investigate whether active muscles are required for knee buckling and to what extent ground reaction forces (GRFs) drive it. In a systematic parameter search, we tested how long the knee muscles vastus (VAS), gastrocnemius (GAS), and hamstrings could be deactivated while maintaining a stable gait with impulsive push-off. VAS deactivation up to 35% of the gait cycle resulted in a dynamic gait with increased ankle peak power. GAS deactivation up to 20% of the gait cycle was detrimental to gait efficiency and showed reduced ankle peak power. At the start of knee buckling, the GRF vector is positioned near the knee joint’s neutral axis, assisting in knee flexion. However, this mechanism is likely not enough to drive knee flexion independently. Our findings contribute to the biomechanical understanding of ankle push-off, with applications in prosthetic and bipedal robotic design, and fundamental research on human gait mechanics.

Separating deterministic and stochastic gravitational wave signals in realistic pulsar timing array datasets

Recent observations by pulsar timing arrays (PTAs) suggest the presence of gravitational wave (GW) signals that potentially originate from supermassive black hole binaries (SMBHBs). These binaries can generate two kinds of signals: a stochastic gravitational wave background (GWB) or a deterministic continuous gravitational wave (CGW). The ability to correctly recognize and separate them is crucial for accurate signal recovery and astrophysical interpretation. This paper is aimed at investigating the interaction between stochastic GWB and deterministic CGW signals with the analysis pipelines currently available. We focus on understanding potential misinterpretations and biases in the parameter estimation when these signals are analysed separately or altogether. To this end, we performed several realistic simulations based on the European PTA 24.8yr dataset. We first injected either a GWB or a CGW into five datasets (of three GWB realisations and two CGW realisations) with identical noise. We analysed each signal type independently and then we analysed data sets containing both a stochastic GWB and a single resolvable CGW. We compared the parameter estimations using different search models, including Earth term (ET) only or combined Earth and pulsar term (ET+PT) CGW templates, along with correlated or uncorrelated power law GWB templates. We show that when searched for independently, the GWB and CGW signals can be misinterpreted (i.e. they can be confused with each other) and only a combined search is able to recover the true signal present. For datasets containing both a GWB and a CGW, failure to account for the latter biases the recovery of the GWB; however, when we perform a combined search, both GWB and CGW parameters can be recovered without any strong bias. Care must be taken with the method used to perform combined searches on these multi-component datasets, as the CGW PT can be misinterpreted as a common uncorrelated red noise. However, this can be avoided by conducting direct searches for a correlated GWB plus a CGW (ET+PT). Our study underscores the importance of combined searches to ensure unbiased recovery of GWB parameters in the presence of strong CGWs. This is crucial to accurately interpreting the signal recently found in PTA data and it is a first step towards a robust framework for disentangling stochastic and deterministic GW components in more sensitive datasets in the future.

Integration of Robust Control and Multi‐Objective Metaheuristic Optimization for Improved Stability and Tracking Performance of WIP Systems

ABSTRACTThe Wheeled Inverted Pendulum (WIP) system presents a challenging control problem in the field of control systems due to its inherent instability, non‐linearity, multivariable nature, and under actuation. This paper introduces a novel framework that integrates a robust combined control algorithm with the Inverted Pendulum Multi‐objective Optimization (IPMO) algorithm to improve the stability and tracking performance of WIP systems. The robust control algorithm combines Time‐Varying Sliding Mode Control (TVSMC), Exponential Reaching Law (ERL), and Non‐linear Feedback Control (NFC) to mitigate chattering and enhance stability. The IPMO algorithm utilizes a Solutions Validity Table (SVT) to balance exploration and exploitation in the search for optimal controller parameters. By optimizing the controller parameters, the aim is to minimize tracking errors and improve the overall performance of the WIP system. Extensive simulations are conducted to demonstrate the effectiveness of the proposed framework in enhancing the WIP system performance and minimizing tracking errors. The proposed IPMO algorithm provides decision‐makers with a range of optimal solutions, contributing to the advancement of control techniques for unstable, highly non‐linear, multivariable, and underactuated systems. A supplemental animated simulation of this work is available at https://www.youtube.com/watch?v=PO‐RzmTXemI.

Nozzle Pressure- and Screw Position-Based CAE Scientific Process Parameter Setup for Injection Molding Process.

This study developed a scientific process parameter setup based on nozzle pressure and screw position, with the process parameter search sequence being injection speed, V/P switchover position, packing pressure, and packing time. Unlike previous studies, this study focuses on the scientific process parameter setup of experiments and simulations, as well as on the implementation of calibration. Experiments and simulations had the same trend of results in the scientific process parameter setup. Although the experiments and simulations had the same trend, the machine response caused parameter errors. After setting the time constant of the simulations, injection speed profiles from the experiments and simulations became closely aligned. The simulation results for the injection speed and V/P switchover position became closer to the experiment results than the results of the uncalibrated simulation. The error between the simulated and experimental injection speed was reduced from 20% to 6% after applying time constant calibration. The V/P switchover point error was also reduced from 11% to 5%, highlighting the effectiveness of the time constant to calibrate the simulation.

Slower swimming promotes chemotactic encounters between bacteria and small phytoplankton

Chemotaxis enables marine bacteria to increase encounters with phytoplankton cells by reducing their search times, provided that bacteria detect noisy chemical gradients around phytoplankton. Gradient detection depends on bacterial phenotypes and phytoplankton size: large phytoplankton produce spatially extended but shallow gradients, whereas small phytoplankton produce steeper but spatially more confined gradients. To date, it has remained unclear how phytoplankton size and bacterial swimming speed affect bacteria's gradient detection ability and search times for phytoplankton. Here, we compute an upper bound on the increase in bacterial encounter rate with phytoplankton due to chemotaxis over random motility alone. We find that chemotaxis can substantially decrease search times for small phytoplankton, but this advantage is highly sensitive to variations in bacterial phenotypes or phytoplankton leakage rates. By contrast, chemotaxis toward large phytoplankton cells reduces the search time more modestly, but this benefit is more robust to variations in search or environmental parameters. Applying our findings to marine phytoplankton communities, we find that, in productive waters, chemotaxis toward phytoplankton smaller than 2 μm provides little to no benefit, but can decrease average search times for large phytoplankton (∼20 μm) from 2 wk to 2 d, an advantage that is robust to variations and favors bacteria with higher swimming speeds. By contrast, in oligotrophic waters, chemotaxis can reduce search times for picophytoplankton (∼1 μm) up to 10-fold, from a week to half a day, but only for bacteria with low swimming speeds and long sensory timescales. This asymmetry may promote the coexistence of diverse search phenotypes in marine bacterial populations.

A Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features.

With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environments, non-line-of-sight (NLOS) propagation reduces the measurement accuracy of 5G signals, causing large deviations in position solving. In order to obtain high-precision position information, it is necessary to recognize the propagation state of the signal before distance measurement or angle measurement. In this paper, we propose a dynamic multi-level optimization of comprehensive features (DMOCF) network model for line-of-sight (LOS)/NLOS identification. The DMOCF model improves the expression ability of the deep model by adding a res2 module to the time delay neural network (TDNN), so that fine-grained feature information such as weak reflections or noise in the signal can be deeply understood by the model, enabling the network to realize layer-level feature processing by adding Squeeze and Excitation (SE) blocks with adaptive weight adjustment for each layer. A mamba module with position coding is added to each layer to capture the local patterns of wireless signals under complex propagation phenomena by extracting local features, enabling the model to understand the evolution of signals over time in a deeper way. In addition, this paper proposes an improved sand cat search algorithm for network parameter search, which improves search efficiency and search accuracy. Overall, this new network architecture combines the capabilities of local feature extraction, global feature preservation, and time series modeling, resulting in superior performance in the 5G channel impulse response (CIR) signal classification task, improving the accuracy of the model and accurately identifying the key characteristics of multipath signal propagation. Experimental results show that the NLOS/LOS recognition method proposed in this paper has higher accuracy than other deep learning methods.

A Motion Target Refocusing Method Based on Range Frequency Difference Processing Without Parameter Search

A Motion Target Refocusing Method Based on Range Frequency Difference Processing Without Parameter Search

Research focus and theme evolution on enhanced external counterpulsation: A bibliometric analysis.

The study delved into the identification of key research areas and evolving trends within the domain of Enhanced External Counterpulsation, aiming to gain comprehensive insights into the subject matter. Utilizing the sophisticated search parameter of 'topic' (TS) on the Web of Science (WoS) database, the necessary information was retrieved. This research employed an array of tools for effective data extraction, analysis, and visualization, which included Microsoft Excel for tabular management, HistCite Pro for citation analysis, GunnMap for geographical mapping, BibExcel for bibliometric assessments, and VOSviewer for network visualization purposes. From its establishment up until March 31, 2024 a total of 535 entries were recorded in the WoS database, with 491 of these relevant to the specified subject matter. The USA was the most prolific country, and China ranked second. Sun Yat Sen University was the most productive institution, which was from China. And 80% of the top 10 institutions were from the USA. A keyword co-occurrence analysis was conducted, revealing four distinct research foci. And they were the application of EECP in cardiovascular disease, the main indications of EECP, the mechanism of EECP and the therapeutic effect of EECP, respectively. Researchers paid the most attention to the application of EECP on the cardiac cardiovascular system. Professor Lawson WE, Hui JCK and Kennard ED were the top three prolific authors. Lawson WE, Henry TD, Braith RW and Zheng ZS were identified as the core authors in the author co-citation network analysis. A comprehensive analysis was conducted on the literature surrounding improved enhanced external counterpulsation in this investigation, which can help researchers understand the theme trend better and grasp the research gap, to carry out further research and promote the progress of the topic.

Global Research Trends in Cervical Cancer Survival in the Last Two Decades: A Bibliometric Analysis.

Cervical cancer poses a significant challenge to global health, particularly in low- and middle-income countries. Tracking the survival rates of cancer patients through data gathered by population-based cancer registries constitutes a crucial aspect of cancer management. In recent years, attention within bibliometric studies has been directed towards examining the correlation between inflammation and radiotherapy in cervical cancer. However, to date, there is no published literature investigating research trends in cervical cancer survival through bibliometric analysis. Thus, this study was undertaken to identify and analyse global research patterns and hotpots in this area. A systematic search was performed within the Web of Science Core Collection (WoSCC) database, employing the following search parameters: TITLE - (((survival) OR (survival rate) OR (survival analysis) OR (survival probability)) AND ((cervical cancer) OR (cervical carcinoma))). A comprehensive analysis of research trends was conducted utilising various tools on the WoSCC website and VOSviewer. A total of 840 papers pertaining to cervical cancer and survival were identified from 45 distinct departments or subjects. Notably, the People's Republic of China and the USA collectively accounted for half of all publications from 2000 to 2023. An extensive cohort comprising 4,759 authors affiliated with 1,454 institutions across 82 countries contributed to the progression of this research domain. Despite a substantial increase in research on cervical cancer survival over the last decade, it is essential to encourage and conduct research, particularly in high-risk regions, especially in countries classified as low- or middle-income.

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework.

Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. In recent years, manual model tuning has been gradually replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. We quantified the performance of the algorithms in terms of the best solutions found and in terms of convergence speed. We identified several algorithms, including covariance matrix adaptation evolution strategy and particle swarm optimization, that consistently, without any fine-tuning, found good solutions in all of our use cases. By contrast, some other algorithms including all local search methods provided good solutions only for the simplest use cases, and failed completely on more complex problems. We also demonstrate the versatility of Neuroptimus by applying it to an additional use case that involves tuning the parameters of a subcellular model of biochemical pathways. Finally, we created an online database that allows uploading, querying and analyzing the results of optimization runs performed by Neuroptimus, which enables all researchers to update and extend the current benchmarking study. The tools and analysis we provide should aid members of the neuroscience community to apply parameter search methods more effectively in their research.

The FunCoup Cytoscape App: multi-species network analysis and visualization.

Functional association networks, such as FunCoup, are crucial for analyzing complex gene interactions. To facilitate the analysis and visualization of such genome-wide networks, there is a need for seamless integration with powerful network analysis tools like Cytoscape. The FunCoup Cytoscape App integrates the FunCoup web service API with Cytoscape, allowing users to visualize and analyze gene interaction networks for 640 species. Users can input gene identifiers and customize search parameters, employing various network expansion algorithms like group or independent gene search, MaxLink, and TOPAS. The app maintains consistent visualizations with the FunCoup website, providing detailed node and link information, including tissue and pathway gene annotations. The integration with Cytoscape plugins, such as ClusterMaker2, enhances the analytical capabilities of FunCoup, as exemplified by the identification of the Myasthenia gravis disease module along with potential new therapeutic targets. The FunCoup Cytoscape App is developed using the Java OSGi framework, with UI components implemented in Java Swing and build support from Maven. The App is available as a JAR file at https://bitbucket.org/sonnhammergroup/funcoup_cytoscape/ repository, and can be downloaded from the Cytoscape App store https://apps.cytoscape.org/apps/funcoup. Supplementary data are available at Bioinformatics online.

Asynchronous parallel reinforcement learning for optimizing propulsive performance in fin ray control

AbstractFish fin rays constitute a sophisticated control system for ray-finned fish, facilitating versatile locomotion within complex fluid environments. Despite extensive research on the kinematics and hydrodynamics of fish locomotion, the intricate control strategies in fin-ray actuation remain largely unexplored. While deep reinforcement learning (DRL) has demonstrated potential in managing complex nonlinear dynamics; its trial-and-error nature limits its application to problems involving computationally demanding environmental interactions. This study introduces a cutting-edge off-policy DRL algorithm, interacting with a fluid–structure interaction (FSI) environment to acquire intricate fin-ray control strategies tailored for various propulsive performance objectives. To enhance training efficiency and enable scalable parallelism, an innovative asynchronous parallel training (APT) strategy is proposed, which fully decouples FSI environment interactions and policy/value network optimization. The results demonstrated the success of the proposed method in discovering optimal complex policies for fin-ray actuation control, resulting in a superior propulsive performance compared to the optimal sinusoidal actuation function identified through a parametric grid search. The merit and effectiveness of the APT approach are also showcased through comprehensive comparison with conventional DRL training strategies in numerical experiments of controlling nonlinear dynamics.

New Metaheuristics to Solve the Internet Shopping Optimization Problem with Sensitive Prices

In this research, two new methods for solving the Internet shopping optimization problem with sensitive prices are proposed, incorporating adaptive adjustment of control parameters. This problem is classified as NP-hard and is relevant to current electronic commerce. The first proposed solution method corresponds to a Memetic Algorithm incorporating improved local search and adaptive adjustment of control parameters. The second proposed solution method is a particle swarm optimization algorithm that adds a technique for diversification and adaptive adjustment of control parameters. We assess the effectiveness of the proposed algorithms by comparing them with the Branch and Bound algorithm, which presents the most favorable outcomes of the state-of-the-art method. Nine instances of three different sizes are used: small, medium, and large. For performance validation, the Wilcoxon and Friedman non-parametric tests are applied. The results show that the proposed algorithms exhibit comparable performance and outperform the Branch and Bound algorithm.

Exploration of global community-based medical education curricula: a scoping review

Background: Community-Based Medical Education (CBME) offers learning and training for medical students in the community setting. This scoping review aimed to describe the characteristics of the CBME curriculum delivered to medical undergraduates globally and to summarize the reported outcomes and impacts. Material and methods: A scoping review was carried out to address the study objectives, using the five-step methodological framework of H. Arksey and L. O’Malley. Published articles on CBME were retrieved using a systematically prebuilt search strategy applying Boolean operators. These search parameters were: no time limit; articles published other than on the medical profession; review articles; and non-English language articles which were excluded. PRISMA-ScR checklist was followed for reporting. Results: Of the 36 articles selected for scoping review, 17 (47.2%) were published between 2010 and 2020; 13 (36.1%) were from the South-East Asian region; and 18 (50%) were descriptive. Cognitive component on health issues was commonly taught (55.6%), 44.4% had CBME in multiple semesters, taught by the Community Medicine faculty, and each had a family survey as a teaching method. Taught in a rural setting among 83.4% of studies, 27.8% had CBME exposure throughout the course, and 47.2% were posted at primary health care. The most common formative and summative assessments were reflections/feedback, and presentations (19.4% each). Conclusions: The components of CBME curricula vary widely across the globe, shaped by the specific contexts of the universities and countries in which they are implemented. Recognizing and understanding these diverse approaches is essential for designing CBME curricula that are holistic, context-specific, and effectively tailored to meet local needs.

Диагностическая эффективность и прогностическая значимость антропометрических параметров как маркеров гиперлептинемии у подростков

Leptin is an energy homeostasis marker synthesized by cells of adipose tissue (adipocytes) and gastric mucosa. The search for clinical and anthropometric parameters associated with energy homeostasis disorders in excess body weight (BW) and obesity is necessary for stratification of patients at risk for early implementation of energy homeostasis disorders. The purpose of this re-search was to evaluate the relationship between serum leptin concentration and anthropometric parameters reflecting the topography of subcutaneous fat in adolescents of different ethnicities with excess BW and obesity in order to identify the most informative anthropometric parameters of energy homeosta-sis disorders. Materials and methods used: a single-center cross-sectional study of 362 adolescents aged 11 to 17 y/o residing in the Republic of Buryatia and the Irkutsk Oblast of Russia was conducted in 2015-2016 that included the participants as follows: 186 Caucasians (46 boys/140 girls) and 176 Mongoloids (96 boys/80 girls); 136 adolescents in the control group (38%) and 226 adolescents with excess BW and obesity (62%). All par-ticipants had their linear height, BW, waist (WC) and hip (HC) circumference, skinfold thickness measured as well as the body mass index (BMI, in kg per m2) calculated. The concentrations of glucose, total cholesterol, high-density, low-density and very low-density lipoprotein cholesterol, triglycerides and in-sulin were determined. The HOMA index was calculated. Serum leptin was de-termined by enzyme immunoassay using DBC Leptin ELISA kit (Diagnostics Biochem Canada Inc., Canada) with detection using BioTek Elx808 Microplate Spectrophotometer (Agilent Technologies, Inc., USA). Results: a strong posi-tive correlation was found between serum leptin levels and SDS BMI and trip-onderal BMI in adolescents of both ethnic groups: in Caucasian boys r=0.747 (p<0.001), r=0.767 (p<0.001), in Caucasian girls r=0.687 (p<0.001) and r=0.695 (p<0.001); in Mongoloid boys r=0.765 (p<0.001), r=0.852 (p<0.001), in Mongoloid girls r=0.612 (p<0.001), r=0.667 (p<0.001), respectively. Ac-cording to the results of ROC analysis, AUC WC/height showed good diagnos-tic significance for detecting hyperleptinemia (AUC=0.8-0.9) in adolescents from all the studied groups. Conclusion: the triponderal index and the WC/height ratio are informative anthropometric indicators for energy metabo-lism disorders and hyperleptinemia in Caucasian and Mongoloid adolescents of both genders with excess BW and obesity.

Bayesian Nonparametric Sequential Search

Sequential search models are popular in marketing for studying consumer search behavior. Current search models use parametric assumptions regarding different aspects of the models, such as random shocks, search costs, and consumer preferences. These assumptions can be restrictive. The authors develop a novel Bayesian nonparametric framework for sequential search to flexibly model unknown distributions. They also develop Markov chain Monte Carlo methods for inferring the model unknowns. The authors conduct simulation studies to demonstrate that currently popular parametric search models can yield incorrect estimates and inferences when the data-generating process deviates from their assumptions. In contrast, the proposed model accurately recovers these quantities for various data-generating processes. The methodology is then applied to online search and purchase data from a Japanese retailer. The results show that several heterogeneity distributions have complex patterns, such as multimodality and skewness, that are not captured by a parametric benchmark model. The authors also estimate the monetary value of search costs, the price elasticities, and a counterfactual profit gain under a personalized couponing strategy and find substantial differences in the results from the two models.

The incubation revolution: transforming entrepreneurial education with artificial intelligence

Purpose Universities face challenges due to the absence of artificial intelligence (AI) integration in entrepreneurship education (EE) and its incubation centers for young startups. Making a business plan for their innovative enterprises, which includes market analysis, financial projections, marketing strategy and an operations plan, are a few of the toughest tasks they may face. Aspiring students can make it simple to launch their dream business by integrating AI tools. Hence, this study aims to conduct a systematic literature review (SLR) to examine the global trend of the transformation of EE with AI and determine the necessity of integrating AI in university incubation centers as a potential future research direction. Design/methodology/approach In this study, the authors conducted an SLR method to investigate the transformation of EE with AI. This review employed a bibliometric analysis covering the period of 1993–2023 and utilized articles published in scientific journals available in the SCOPUS database as our data source. Findings There is an enormous potential for research on EE using cutting-edge AI in developed and developing nations. There is a lack of studies exploring AI integration into university incubation centers. Hence, there are possible future directions for research into integrating AI into university incubation centers using cutting-edge tools like chatbots, ChatGPT, ChatGen and other AI that will help to develop a comprehensive business plan for students aspiring entrepreneurial venture startups. Research limitations/implications The study’s research was limited using the Scopus database’s core collection, which may ignore other significant research articles. Therefore, the study’s scope can be constrained due to the narrow search parameters. The study, however, tries to establish the importance of its research by offering a thorough review and evaluation of AI in EE. Practical implications There is significance of incorporating AI into EE to foster an EE culture and realize its potential benefits. To transform incubation centers and promote aspirant entrepreneurs in the fourth industrial revolution (4IR), higher education institutions (HEIs) should strategically adopt AI. Originality/value This study presents a novel viewpoint by investigating the distinction in AI perception and usage among educators, advocating the incorporation of AI in university incubation centers to help entrepreneurial students. It contributes uniqueness and innovative approaches to early startup issues in EE.